Total & Trajectory
- K-means Clustering
- Regression results
Trajectory
- K-means Clustering
- Regression results
Total
- K-means Clustering
- Regression results

In this script, you can find the results of the analysis across the corpus callosum. To examine individual differences, we used K-means clustering in an exploratory analysis:

K-means clustering was conducted based on the total level of brain connectivity (total; FA at imaging timepoint one plus timepoint two: higher FA total level implies greater connectivity, lower FA total levels imply lower connectivity) and the development of brain connectivity from imaging timepoint one to two (trajectory; FA at timepoint two minus timepoint one).
Multinomial logistic regression was used to predict cluster membership based on adversity experiences at baseline. To adjust for multiple comparisons, p-values were Bonferroni-corrected in the actual paper. We corrected for the number of brain regions (four) and adversity scales (five).

Total & Trajectory

K-means Clustering

set.seed(2111)
Kmeans_CC <- kmeans(AdvDTI[,19:20], 4, iter.max = 10000, nstart = 50, algorithm="Lloyd")

Groups <- as.factor(Kmeans_CC$cluster)
samplesize <- 10808
id=factor(1:samplesize)

PlotCluster <- data.frame(c(AdvDTI$CC_FA_t1),     #select FA at time 1
                              c(AdvDTI$CC_FA_t2),     #select FA at time 2
                              as.factor(c(id,id)),         #Give ID to participants
                              as.factor(Groups))               #insert cluster number
colnames(PlotCluster)<-c('T1','T2','ID','Cluster')  #give column name

PlotAllClusters<-melt(PlotCluster,by='ID')

ggplot(PlotAllClusters,aes(variable,value,group=ID,col=Cluster))+
  geom_point(position = "jitter", size=1,alpha=.3)+
  geom_line(position = "jitter", alpha=.1)+
  stat_smooth(aes(group=Cluster), colour="black", method="loess", se=F, size=0.5, formula = 'y~x')+ 
  ylab('FA per participant')+
  xlab('Timepoints')+
  facet_grid(~factor(Cluster, levels=c("1","2","3","4")), 
             labeller = as_labeller(c("1" = "Cluster 1", "2" = "Cluster 2", "3" = "Cluster 3", "4" = "Cluster 4")))+
  scale_color_manual(values = my_colors,
                     breaks =c("1","2","3","4"))+
  guides(col = FALSE)+
  theme(axis.title = element_text(size = 14),
        axis.text = element_text(size = 12),
        strip.text = element_text(size = 14))

Regression results

AdvDTI$Groups <- as.factor(Kmeans_CC$cluster)
AdvDTI[,2:20] <- apply(AdvDTI[,2:20],2, function(x) as.numeric(as.character(x)))

mod_PTSD_CC = multinom(Groups ~ PTSDSum, data=AdvDTI)
mod_School_CC = multinom(Groups ~ SchoolSum, data=AdvDTI)
mod_Family_CC = multinom(Groups ~ FamilySum, data=AdvDTI)
mod_Safety_CC = multinom(Groups ~ Safety, data=AdvDTI)
mod_SES_CC = multinom(Groups ~ SES, data=AdvDTI)

options(scipen = 999)

Anova(mod_PTSD_CC, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: Groups
##         LR Chisq Df Pr(>Chisq)
## PTSDSum   4.0034  3     0.2611

summary(mod_PTSD_CC)

## Call:
## multinom(formula = Groups ~ PTSDSum, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)       PTSDSum
## 2 -0.05504257 -0.0144205561
## 3 -0.95933865 -0.0001922317
## 4 -1.14673339  0.0474860624
## 
## Std. Errors:
##   (Intercept)    PTSDSum
## 2  0.02244705 0.02335901
## 3  0.02973852 0.03043780
## 4  0.03188719 0.02981463
## 
## Residual Deviance: 27442.85 
## AIC: 27454.85

zPTCC <- summary(mod_PTSD_CC)$coefficients/summary(mod_PTSD_CC)$standard.errors
zPTCC

##   (Intercept)     PTSDSum
## 2   -2.452107 -0.61734442
## 3  -32.259129 -0.00631556
## 4  -35.962194  1.59271031

pPTCC <- (1 - pnorm(abs(zPTCC), 0, 1)) * 2
pPTCC

##   (Intercept)   PTSDSum
## 2  0.01420226 0.5370076
## 3  0.00000000 0.9949609
## 4  0.00000000 0.1112252

Anova(mod_School_CC, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: Groups
##           LR Chisq Df Pr(>Chisq)
## SchoolSum  0.93765  3     0.8163

summary(mod_School_CC)

## Call:
## multinom(formula = Groups ~ SchoolSum, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)    SchoolSum
## 2 -0.05480903  0.016004625
## 3 -0.95940772 -0.004463833
## 4 -1.14571188  0.020477267
## 
## Std. Errors:
##   (Intercept)  SchoolSum
## 2  0.02244592 0.02260664
## 3  0.02974209 0.03008998
## 4  0.03186658 0.03199942
## 
## Residual Deviance: 27445.91 
## AIC: 27457.91

zSCC <- summary(mod_School_CC)$coefficients/summary(mod_School_CC)$standard.errors
zSCC

##   (Intercept)  SchoolSum
## 2   -2.441826  0.7079612
## 3  -32.257573 -0.1483494
## 4  -35.953402  0.6399263

pSCC <- (1 - pnorm(abs(zSCC), 0, 1)) * 2
pSCC

##   (Intercept) SchoolSum
## 2  0.01461317 0.4789694
## 3  0.00000000 0.8820670
## 4  0.00000000 0.5222205

Anova(mod_Family_CC, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: Groups
##           LR Chisq Df Pr(>Chisq)   
## FamilySum   14.986  3   0.001828 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_Family_CC)

## Call:
## multinom(formula = Groups ~ FamilySum, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)   FamilySum
## 2  -0.0555754 -0.08578408
## 3  -0.9589762 -0.05647759
## 4  -1.1451671 -0.03078163
## 
## Std. Errors:
##   (Intercept)  FamilySum
## 2  0.02246666 0.02257133
## 3  0.02974835 0.02982602
## 4  0.03187001 0.03168774
## 
## Residual Deviance: 27431.86 
## AIC: 27443.86

zFCC <- summary(mod_Family_CC)$coefficients/summary(mod_Family_CC)$standard.errors
zFCC

##   (Intercept)  FamilySum
## 2   -2.473684 -3.8005767
## 3  -32.236277 -1.8935679
## 4  -35.932435 -0.9714052

pFCC <- (1 - pnorm(abs(zFCC), 0, 1)) * 2
pFCC

##   (Intercept)    FamilySum
## 2  0.01337281 0.0001443597
## 3  0.00000000 0.0582823911
## 4  0.00000000 0.3313465187

Anova(mod_Safety_CC, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: Groups
##        LR Chisq Df Pr(>Chisq)  
## Safety   10.239  3    0.01664 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_Safety_CC)

## Call:
## multinom(formula = Groups ~ Safety, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)         Safety
## 2 -0.05555568 -0.06824495927
## 3 -0.95906929 -0.02812802220
## 4 -1.14576658  0.00003052823
## 
## Std. Errors:
##   (Intercept)     Safety
## 2  0.02245926 0.02270873
## 3  0.02974083 0.02983788
## 4  0.03187434 0.03164558
## 
## Residual Deviance: 27436.61 
## AIC: 27448.61

zSafCC <- summary(mod_Safety_CC)$coefficients/summary(mod_Safety_CC)$standard.errors
zSafCC

##   (Intercept)        Safety
## 2   -2.473621 -3.0052294717
## 3  -32.247564 -0.9426949950
## 4  -35.946367  0.0009646918

pSafCC <- (1 - pnorm(abs(zSafCC), 0, 1)) * 2
pSafCC

##   (Intercept)      Safety
## 2  0.01337517 0.002653806
## 3  0.00000000 0.345836934
## 4  0.00000000 0.999230287

Anova(mod_SES_CC, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: Groups
##     LR Chisq Df Pr(>Chisq)    
## SES   23.643  3 0.00002965 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_SES_CC)

## Call:
## multinom(formula = Groups ~ SES, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)         SES
## 2 -0.05667253 -0.04823379
## 3 -0.96183836  0.08531301
## 4 -1.14632337  0.05027420
## 
## Std. Errors:
##   (Intercept)        SES
## 2  0.02246837 0.02303971
## 3  0.02979329 0.02901448
## 4  0.03188172 0.03150313
## 
## Residual Deviance: 27423.21 
## AIC: 27435.21

zSESCC <- summary(mod_SES_CC)$coefficients/summary(mod_SES_CC)$standard.errors
zSESCC

##   (Intercept)       SES
## 2   -2.522325 -2.093507
## 3  -32.283722  2.940359
## 4  -35.955501  1.595848

pSESCC <- (1 - pnorm(abs(zSESCC), 0, 1)) * 2
pSESCC

##   (Intercept)         SES
## 2   0.0116582 0.036303911
## 3   0.0000000 0.003278318
## 4   0.0000000 0.110522771

Trajectory

K-means Clustering

set.seed(2343)
Kmeans_Shape <- kmeans(AdvDTI$CC_FA_Shape, 3, iter.max = 10, nstart = 50)

ShapeGroups <- as.factor(Kmeans_Shape$cluster)

ShapePlotCluster <- data.frame(c(AdvDTI$CC_FA_t1),     
                              c(AdvDTI$CC_FA_t2),   
                              as.factor(c(id,id)),  
                              as.factor(ShapeGroups))    

colnames(ShapePlotCluster)<-c('T1', 'T2','ID','Cluster')
ShapePlotAllClusters<-melt(ShapePlotCluster,by='ID')

ggplot(ShapePlotAllClusters,aes(variable,value,group=ID,col=Cluster))+
  geom_point(position = "jitter", size=1,alpha=.3)+
  geom_line(position = "jitter", alpha=.1)+
  stat_smooth(aes(group=Cluster), colour="black", method="loess", se=F, size=0.5, formula = 'y~x')+ 
  ylab('FA per participant')+
  xlab('Timepoints')+
  facet_grid(~factor(Cluster, levels=c("1","2","3")), 
             labeller = as_labeller(c("1" = "Cluster 1", "2" = "Cluster 2", "3" = "Cluster 3")))+
  scale_color_manual(values = my_colors,
                     breaks =c("1","2","3"))+
  guides(col = FALSE)+
  theme(axis.title = element_text(size = 14),
        axis.text = element_text(size = 12),
        strip.text = element_text(size = 14))

Regression results

mod_PTSD_Shape = multinom(ShapeGroups ~ PTSDSum, data=AdvDTI)
mod_School_Shape = multinom(ShapeGroups ~ SchoolSum, data=AdvDTI)
mod_Family_Shape = multinom(ShapeGroups ~ FamilySum, data=AdvDTI)
mod_Safety_Shape = multinom(ShapeGroups ~ Safety, data=AdvDTI)
mod_SES_Shape = multinom(ShapeGroups ~ SES, data=AdvDTI)

Anova(mod_PTSD_Shape, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: ShapeGroups
##         LR Chisq Df Pr(>Chisq)  
## PTSDSum   6.0806  2    0.04782 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_PTSD_Shape)

## Call:
## multinom(formula = ShapeGroups ~ PTSDSum, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)     PTSDSum
## 2    1.295534 -0.06223648
## 3    0.171315 -0.03952417
## 
## Std. Errors:
##   (Intercept)    PTSDSum
## 2  0.02624678 0.02493114
## 3  0.03156409 0.03015473
## 
## Residual Deviance: 19871.6 
## AIC: 19879.6

zPTShape <- summary(mod_PTSD_Shape)$coefficients/summary(mod_PTSD_Shape)$standard.errors
zPTShape

##   (Intercept)   PTSDSum
## 2   49.359717 -2.496335
## 3    5.427527 -1.310712

pPTShape <- (1 - pnorm(abs(zPTShape), 0, 1)) * 2
pPTShape

##        (Intercept)    PTSDSum
## 2 0.00000000000000 0.01254839
## 3 0.00000005714019 0.18995512

Anova(mod_School_Shape, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: ShapeGroups
##           LR Chisq Df Pr(>Chisq)
## SchoolSum  0.13255  2     0.9359

summary(mod_School_Shape)

## Call:
## multinom(formula = ShapeGroups ~ SchoolSum, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)   SchoolSum
## 2    1.294815 0.008551055
## 3    0.170455 0.010780702
## 
## Std. Errors:
##   (Intercept)  SchoolSum
## 2  0.02622922 0.02648226
## 3  0.03155011 0.03181533
## 
## Residual Deviance: 19877.55 
## AIC: 19885.55

zSShape <- summary(mod_School_Shape)$coefficients/summary(mod_School_Shape)$standard.errors
zSShape

##   (Intercept) SchoolSum
## 2   49.365374 0.3228975
## 3    5.402677 0.3388524

pSShape <- (1 - pnorm(abs(zSShape), 0, 1)) * 2
pSShape

##        (Intercept) SchoolSum
## 2 0.00000000000000 0.7467729
## 3 0.00000006565352 0.7347209

Anova(mod_Family_Shape, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: ShapeGroups
##           LR Chisq Df Pr(>Chisq)
## FamilySum   0.4241  2     0.8089

summary(mod_Family_Shape)

## Call:
## multinom(formula = ShapeGroups ~ FamilySum, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)    FamilySum
## 2   1.2947374 -0.006155084
## 3   0.1701906 -0.019427289
## 
## Std. Errors:
##   (Intercept)  FamilySum
## 2  0.02622765 0.02621389
## 3  0.03155144 0.03164674
## 
## Residual Deviance: 19877.26 
## AIC: 19885.26

zFShape <- summary(mod_Family_Shape)$coefficients/summary(mod_Family_Shape)$standard.errors
zFShape

##   (Intercept)  FamilySum
## 2   49.365355 -0.2348024
## 3    5.394068 -0.6138796

pFShape <- (1 - pnorm(abs(zFShape), 0, 1)) * 2
pFShape

##        (Intercept) FamilySum
## 2 0.00000000000000 0.8143621
## 3 0.00000006888014 0.5392949

Anova(mod_Safety_Shape, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: ShapeGroups
##        LR Chisq Df Pr(>Chisq)
## Safety   0.8205  2     0.6635

summary(mod_Safety_Shape)

## Call:
## multinom(formula = ShapeGroups ~ Safety, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)      Safety
## 2   1.2947483 -0.02031187
## 3   0.1704031 -0.00450179
## 
## Std. Errors:
##   (Intercept)     Safety
## 2  0.02622885 0.02628586
## 3  0.03154985 0.03158046
## 
## Residual Deviance: 19876.86 
## AIC: 19884.86

zSafShape <- summary(mod_Safety_Shape)$coefficients/summary(mod_Safety_Shape)$standard.errors
zSafShape

##   (Intercept)     Safety
## 2   49.363523 -0.7727300
## 3    5.401074 -0.1425499

pSafShape <- (1 - pnorm(abs(zSafShape), 0, 1)) * 2
pSafShape

##        (Intercept)    Safety
## 2 0.00000000000000 0.4396822
## 3 0.00000006624316 0.8866457

Anova(mod_SES_Shape, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: ShapeGroups
##     LR Chisq Df Pr(>Chisq)    
## SES   22.509  2 0.00001295 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_SES_Shape)

## Call:
## multinom(formula = ShapeGroups ~ SES, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)         SES
## 2   1.2937990 -0.05426966
## 3   0.1680632  0.05865348
## 
## Std. Errors:
##   (Intercept)        SES
## 2  0.02623414 0.02634861
## 3  0.03158253 0.03097238
## 
## Residual Deviance: 19855.17 
## AIC: 19863.17

zSESShape <- summary(mod_SES_Shape)$coefficients/summary(mod_SES_Shape)$standard.errors
zSESShape

##   (Intercept)       SES
## 2   49.317387 -2.059678
## 3    5.321397  1.893735

pSESShape <- (1 - pnorm(abs(zSESShape), 0, 1)) * 2
pSESShape

##       (Intercept)        SES
## 2 0.0000000000000 0.03942933
## 3 0.0000001029736 0.05826018

Total

K-means Clustering

set.seed(123)
Kmeans_Magnitude <- kmeans(AdvDTI[,20], 2, iter.max = 10, nstart = 50)   #cluster

MagnitudeGroups <- as.factor(Kmeans_Magnitude$cluster)

MagnitudePlotCluster <- data.frame(c(AdvDTI$CC_FA_t1),     #select FA at time 1
                              c(AdvDTI$CC_FA_t2),     #select FA at time 2
                              as.factor(c(id,id)),         #Give ID to participants
                              as.factor(MagnitudeGroups))               #insert cluster number
colnames(MagnitudePlotCluster)<-c('T1', 'T2','ID','Cluster')  #give column name

MagnitudePlotAllClusters<-melt(MagnitudePlotCluster,by='ID')

ggplot(MagnitudePlotAllClusters,aes(variable,value,group=ID,col=Cluster))+
  geom_point(position = "jitter", size=1,alpha=.3)+
  geom_line(position = "jitter", alpha=.1)+
  stat_smooth(aes(group=Cluster), colour="black", method="loess", se=F, size=0.5, formula = 'y~x')+ 
  ylab('FA per participant')+
  xlab('Timepoints')+
  facet_grid(~factor(Cluster, levels=c("1","2")), 
             labeller = as_labeller(c("1" = "Cluster 1", "2" = "Cluster 2")))+
  scale_color_manual(values = my_colors,
                     breaks =c("1","2","3","4"))+
  guides(col = FALSE)+
  theme(axis.title = element_text(size = 14),
        axis.text = element_text(size = 12),
        strip.text = element_text(size = 14))

Regression results

mod_PTSD_Magnitude = multinom(MagnitudeGroups ~ PTSDSum, data=AdvDTI)
mod_School_Magnitude = multinom(MagnitudeGroups ~ SchoolSum, data=AdvDTI)
mod_Family_Magnitude = multinom(MagnitudeGroups ~ FamilySum, data=AdvDTI)
mod_Safety_Magnitude = multinom(MagnitudeGroups ~ Safety, data=AdvDTI)
mod_SES_Magnitude = multinom(MagnitudeGroups ~ SES, data=AdvDTI)

options(scipen = 999)

Anova(mod_PTSD_Magnitude, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: MagnitudeGroups
##         LR Chisq Df Pr(>Chisq)
## PTSDSum  0.15745  1     0.6915

summary(mod_PTSD_Magnitude)

## Call:
## multinom(formula = MagnitudeGroups ~ PTSDSum, data = AdvDTI)
## 
## Coefficients:
##                   Values  Std. Err.
## (Intercept) -0.071438277 0.01925037
## PTSDSum      0.007759964 0.01955630
## 
## Residual Deviance: 14969.12 
## AIC: 14973.12

zPTMagnitude <- summary(mod_PTSD_Magnitude)$coefficients/summary(mod_PTSD_Magnitude)$standard.errors
zPTMagnitude

## (Intercept)     PTSDSum 
##  -3.7110076   0.3968013

pPTMagnitude <- (1 - pnorm(abs(zPTMagnitude), 0, 1)) * 2
pPTMagnitude

##  (Intercept)      PTSDSum 
## 0.0002064359 0.6915139609

Anova(mod_School_Magnitude, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: MagnitudeGroups
##           LR Chisq Df Pr(>Chisq)
## SchoolSum   1.3067  1      0.253

summary(mod_School_Magnitude)

## Call:
## multinom(formula = MagnitudeGroups ~ SchoolSum, data = AdvDTI)
## 
## Coefficients:
##                  Values  Std. Err.
## (Intercept) -0.07158195 0.01925168
## SchoolSum   -0.02217396 0.01940262
## 
## Residual Deviance: 14967.97 
## AIC: 14971.97

zSMagnitude <- summary(mod_School_Magnitude)$coefficients/summary(mod_School_Magnitude)$standard.errors
zSMagnitude

## (Intercept)   SchoolSum 
##   -3.718218   -1.142833

pSMagnitude <- (1 - pnorm(abs(zSMagnitude), 0, 1)) * 2
pSMagnitude

##  (Intercept)    SchoolSum 
## 0.0002006329 0.2531077353

Anova(mod_Family_Magnitude, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: MagnitudeGroups
##           LR Chisq Df Pr(>Chisq)    
## FamilySum   17.948  1  0.0000227 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_Family_Magnitude)

## Call:
## multinom(formula = MagnitudeGroups ~ FamilySum, data = AdvDTI)
## 
## Coefficients:
##                  Values  Std. Err.
## (Intercept) -0.07093424 0.01926652
## FamilySum    0.08179353 0.01932391
## 
## Residual Deviance: 14951.33 
## AIC: 14955.33

zFMagnitude <- summary(mod_Family_Magnitude)$coefficients/summary(mod_Family_Magnitude)$standard.errors
zFMagnitude

## (Intercept)   FamilySum 
##   -3.681736    4.232763

pFMagnitude <- (1 - pnorm(abs(zFMagnitude), 0, 1)) * 2
pFMagnitude

##   (Intercept)     FamilySum 
## 0.00023165105 0.00002308377

Anova(mod_Safety_Magnitude, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: MagnitudeGroups
##        LR Chisq Df Pr(>Chisq)  
## Safety   3.0548  1     0.0805 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_Safety_Magnitude)

## Call:
## multinom(formula = MagnitudeGroups ~ Safety, data = AdvDTI)
## 
## Coefficients:
##                  Values  Std. Err.
## (Intercept) -0.07134222 0.01925298
## Safety       0.03386783 0.01937806
## 
## Residual Deviance: 14966.23 
## AIC: 14970.23

zSafMagnitude <- summary(mod_Safety_Magnitude)$coefficients/summary(mod_Safety_Magnitude)$standard.errors
zSafMagnitude

## (Intercept)      Safety 
##   -3.705516    1.747741

pSafMagnitude <- (1 - pnorm(abs(zSafMagnitude), 0, 1)) * 2
pSafMagnitude

##  (Intercept)       Safety 
## 0.0002109607 0.0805089454

Anova(mod_SES_Magnitude, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: MagnitudeGroups
##     LR Chisq Df Pr(>Chisq)
## SES   2.2787  1     0.1312

summary(mod_SES_Magnitude)

## Call:
## multinom(formula = MagnitudeGroups ~ SES, data = AdvDTI)
## 
## Coefficients:
##                  Values  Std. Err.
## (Intercept) -0.07113386 0.01925333
## SES          0.02929034 0.01940476
## 
## Residual Deviance: 14967 
## AIC: 14971

zSESMagnitude <- summary(mod_SES_Magnitude)$coefficients/summary(mod_SES_Magnitude)$standard.errors
zSESMagnitude

## (Intercept)         SES 
##   -3.694627    1.509441

pSESMagnitude <- (1 - pnorm(abs(zSESMagnitude), 0, 1)) * 2
pSESMagnitude

##  (Intercept)          SES 
## 0.0002202099 0.1311860336

Ayla Pollmann - 2023

Corpus Callosum

Total & Trajectory

K-means Clustering

Regression results

Trajectory

K-means Clustering

Regression results

Total

K-means Clustering

Regression results